Jennifer C. Dela Cruz, Ramon G. Garcia, Annissa Vi C. Diaz, Angelika Mae B. Diño, Danielle Jane I. Nicdao, Christine Shayne S. Venancio
{"title":"Portable Blood Typing Device Using Image Analysis","authors":"Jennifer C. Dela Cruz, Ramon G. Garcia, Annissa Vi C. Diaz, Angelika Mae B. Diño, Danielle Jane I. Nicdao, Christine Shayne S. Venancio","doi":"10.1109/icce-asia46551.2019.8941604","DOIUrl":null,"url":null,"abstract":"Blood type can be determined by the presence or absence of antigens in the red blood cells, and can be classified by the ABO (A, $B$, AB, O) and Rh D (either positive or negative) systems. Knowing one's blood type is one of the most crucial steps before blood transfusion or any medical operations to prevent the risk of receiving incompatible blood that could lead to adverse or even fatal reactions to patients. Although fully automated blood testing instruments are already being used in some major hospitals, its large size and long processing time, limit its ability to be used in emergency situations. Hence, during onsite blood typing, the traditional or the slide method is being used, which is less accurate due to human errors. This paper presents a raspberry pi based image processing system that is capable of determining all eight types of blood using Canny Edge and Contour Detection. All blood types detected by the proposed system matched that of the known blood samples for the controlled testing of all five samples with five trials each sample for the known A+, $B$+ AB+, O+, A-, B-, AB- and O-. Uncontrolled testing was also performed to compare the results of the ten random blood types identified by the proposed prototype to the results obtained from test tube method. All these ten samples matched the results obtained from the clinical laboratory. This portable and automated device could avoid human errors, without risking accurate results that could be obtain in a short period of time.","PeriodicalId":117814,"journal":{"name":"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icce-asia46551.2019.8941604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Blood type can be determined by the presence or absence of antigens in the red blood cells, and can be classified by the ABO (A, $B$, AB, O) and Rh D (either positive or negative) systems. Knowing one's blood type is one of the most crucial steps before blood transfusion or any medical operations to prevent the risk of receiving incompatible blood that could lead to adverse or even fatal reactions to patients. Although fully automated blood testing instruments are already being used in some major hospitals, its large size and long processing time, limit its ability to be used in emergency situations. Hence, during onsite blood typing, the traditional or the slide method is being used, which is less accurate due to human errors. This paper presents a raspberry pi based image processing system that is capable of determining all eight types of blood using Canny Edge and Contour Detection. All blood types detected by the proposed system matched that of the known blood samples for the controlled testing of all five samples with five trials each sample for the known A+, $B$+ AB+, O+, A-, B-, AB- and O-. Uncontrolled testing was also performed to compare the results of the ten random blood types identified by the proposed prototype to the results obtained from test tube method. All these ten samples matched the results obtained from the clinical laboratory. This portable and automated device could avoid human errors, without risking accurate results that could be obtain in a short period of time.